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All Journal Jurnal Teknik Informatika C.I.T. Medicom Jurnal Pengabdian kepada Masyarakat Nusantara Journal of Accounting Information System
Agustyaningrum, Cucu Ika
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Validation Data Stock Opname Persediaan Berbasis Komputerisasi Untuk Penjualan Mubarok, Husni; Lukman, Afit Muhammad; Hellyana, Corie Mei; Agustyaningrum, Cucu Ika
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 2 (2025): Jurnal Pengabdian kepada Masyarakat Nusantara Edisi April - Juni
Publisher : Lembaga Dongan Dosen

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Abstract

The Tri Dharma of higher education must be fulfilled, including community service which aims to improve the standard of living of the community around the campus domicile. The students of the Riyadush Solihin Islamic boarding school whose learning is focused on religion, feel it is necessary to provide provisions to enrich their scientific knowledge regarding validation of stock taking data for inventory for sales. The analysis method for primary and secondary data uses a descriptive style combining quantitative and qualitative to interpret the characteristics of students towards training. The final results of the training achievements of the 26 respondents were phenomenal. The students were very enthusiastic about learning to validate data on stock taking, inventory for sales, which could add to their scientific knowledge, with an average initial test of 70.77%, the predicate was more than sufficient, which was seen to increase, changing to a final test of 93. .08 very good predicate. Training activities provide benefits with an average score of 4.42% grade A (strongly agree), increase insight with an average score of 4.27% grade A (strongly agree), increase skills with an average score of 4.23% grade A (strongly agree). providing the use of science and technology with an average score of 4.42% grade A (strongly agree), providing solutions to problems with an average score of 4.04% grade A (strongly agree).
Analisis Sentimen Ulasan Google Review Pengguna Layanan Ekspedisi PT. Muna Safira Jaya Utama Menggunakan Metode Naive Bayes Jaya, Al Razad Esmemen; Agustyaningrum, Cucu Ika; Surniandari, Artika; Haryani
JAIS - Journal of Accounting Information System Vol. 5 No. 01 (2025): Juni
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jais.v5i01.8902

Abstract

Perkembangan teknologi informasi yang semakin cepat membawa dampak signifikan pada berbagai sektor bisnis, termaksud di dalamnya adalah jasa ekspedisi. Salah satu cara untuk mengetahui tingkat kepuasan pelanggan terhadap layanan adalah dengan melakukan analisis sentimen. Penelitian ini bertujuan melakukan analisis sentimen pelanggan pada jasa ekspedisi PT. MSJU dengan proses mengklasifikasikan pendapat, sentimen, evaluasi, dan emosi menjadi sentimen positif atau negatif menggunakan algoritma naive bayes. Data yang digunakan untuk analisis dari google review yang diunduh menggunakan instan data scrapper. Tahapan dari analisis sentimen ini berisikan yaitu pengumpulan data, pre-processing, pelabelan sentimen, dan pengklasifikasikan data menggunakan algoritma naive bayes. Algoritma naive bayes memiliki tingkat akurasi yang tinggi. Hasil pengumpulan data dalam kurun waktu 4 - 15 Desember 2024 dengan hasil sebanyak 500 ulasan. Hasil penelitian yang dilakukan dengan algoritma naive bayes didapatkan hasil accuracy sebesar 78.60%. Pada proses evaluasi sentimen positif didapatkan nilai precision mencapai 78,47% dengan recall sebesar 100%, sementara untuk sentimen negatif mendapatkan nilai precision 100% dengan recall sebesar 2,73%. dengan hasil ini bahwa model peneliti memiliki performa yang sangat baik.
Comparative Study of CatBoost, XGBoost, Random Forest, and Decision Tree for Phishing Web Page Classification Haryani, Haryani; Agustyaningrum, Cucu Ika
Jurnal Teknik Informatika C.I.T Medicom Vol 17 No 6 (2026): Computer Science
Publisher : Institute of Computer Science (IOCS)

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Abstract

Phishing is a fraudulent method in which attackers using fake websites steal user information such as login credentials and sensitive financial data. Therefore, this study compares four machine learning algorithms, namely CatBoost, XGBoost, Random Forest, and Decision Tree, in classifying phishing websites efficiently and accurately. In this study, the dataset used is the Web Page Phishing Dataset, which begins with exploration and preprocessing, which includes data cleaning, handling missing values, normalization, feature selection, and testing. Post-split. The data used has been divided into training data and test data, namely 80:20. The model was implemented using Python in Google Colaboratory. Model performance evaluation was measured in five main metrics, such as accuracy, precision, recall, F1-score, and AUC. The experimental results indicate that CatBoost achieved the best position with a performance of 89.57% in accuracy, 85.74% in F1-score, 88.73% in precision, 88.78% in recall, and 89.00% in AUC. XGBoost ranked second with a very competitive performance, followed by Random Forest, which was relatively stable with an accuracy value of 89.41% and an F1-score of 85.35%. On the other hand, the decision tree achieved the lowest performance with an accuracy of 88.69% and an F1-score of 84.10%. These performance results indicate limitations in handling complex data, as well as a tendency to overfit. Overall, ensemble boosting-based algorithms, especially CatBoost and XGBoost, outperform single trees in detecting phishing websites. These results will be benefical to?progress in the next generation for the construction of intelligent based phishing detection system under machine learning. In addition, the outcomes of this study will gain momentum for future works where hyperparameter optimization, larger datasets and real-time applications for phishing detection systems?can be focused. Furthermore, this work will contrast the application of ensemble?algorithm in the cybersecurity field.
Co-Authors Afit Muhammad Lukman - STMIK Nusa Mandiri Jakarta, Afit Muhammad Lukman Artika Surniandari Corie Mei Hellyana Haryani Haryani Haryani Husni Mubarok Jaya, Al Razad Esmemen